There is a growing need for models that are interpretable and have reduced energy and computational cost (e.g., in health care analytics and federated learning). Examples of algorithms to train such models include logistic regression and boosting. However, one challenge facing these algorithms is that they provably suffer from label noise; this has been attributed to the joint interaction between oft-used convex loss functions and simpler hypothesis classes, resulting in too much emphasis being placed on outliers. In this work, we use the margin-based $\alpha$-loss, which continuously tunes between canonical convex and quasi-convex losses, to robustly train simple models. We show that the $\alpha$ hyperparameter smoothly introduces non-convexity and offers the benefit of "giving up" on noisy training examples. We also provide results on the Long-Servedio dataset for boosting and a COVID-19 survey dataset for logistic regression, highlighting the efficacy of our approach across multiple relevant domains.

翻译：随着对可解释性及低能耗与低计算成本模型的需求日益增长（例如在医疗分析和联邦学习中），逻辑回归和提升算法等模型训练方法受到广泛关注。然而，这些算法面临的一大挑战是其确凿存在的标签噪声问题：这归因于常用的凸损失函数与较简单假设类之间的联合作用，导致模型过度关注异常值。本研究采用基于间隔的$\alpha$-损失函数，该损失函数在典型凸损失与拟凸损失之间连续调节，从而实现对简单模型的鲁棒训练。我们证明$\alpha$超参数可平滑引入非凸性，并带来“放弃”含噪声训练样本的优势。此外，我们基于Long-Servedio数据集（用于提升算法）和COVID-19调查数据集（用于逻辑回归）的实验结果，突显了该方法在多个相关领域中的有效性。